Sensor location estimation is important for many location-based systems in ubiquitous environments. Sensor location is usually determined using a global positioning system. For indoor localization, methods that use the received signal strength (RSS) of wireless sensors are used instead of a global positioning system because of the lack of availability of a global positioning system for indoor environments. However, there is a problem in determining sensor locations from the RSS: radio signal interference occurs because of the presence of indoor obstacles. To avoid this problem, we propose a novel localization method that uses environmental data recorded at each sensor location and a data classification technique to identify the location of sensor nodes. In this study, we used a wireless sensor node to collect data on various environmental parameters—temperature, humidity, sound, and light. We then extracted some features from the collected data and trained the location data classifier to identify the location of the wireless sensor node.
Location-aware services are an important application of ubiquitous computing. Therefore, in wireless sensor networks (WSNs), localization has become an essential functionality. Essentially, the localization of a wireless sensor node is achieved by measuring the received signal strength (RSS) of wireless links between the target node and multiple reference nodes and using the theory that the signal strength of the wireless link between two wireless nodes decreases as the distance between them increases. Measured RSS data are used to determine the location of the target node in methods such as triangulation [
In this paper, we propose a novel localization method for sensor nodes in indoor wireless sensor network environments [
The rest of this paper is organized as follows. In Section
Triangulation techniques include RSS indicator (RSSI) [
The use of triangulation methods for indoor environments is very problematic because they use the RSS; the drawback [
A fingerprinting [
eWatch [
eWatch senses light, motion, sound, and temperature and provides visual, sound, and tactile notification. It has ample processing capabilities and a multiday battery life, which allows realistic user studies. This paper describes the motivation for developing a wearable computing platform, a description of power-aware hardware and software architectures and demonstrates the identification and recognition of a set of frequently visited locations via online nearest-neighbor classification.
Figure
Top view of the eWatch board.
In this section, we explain the design of the proposed system and describe the architecture and design concepts. In addition, details of the method for each module will be discussed.
Figure
System architecture.
The collected environmental data of each space is used for training the user location recognition module (ULRM). The location data feature extraction module (LFEM) provides a feature extraction function. This function is applied to the environmental data of the user location provided by the LDCM. The extracted features are input into the ULRM for the purpose of user location recognition. Primarily, feature extraction is used to decrease the amount of high-frequency data. In the LFEM, the data are converted from the format of the ULRM training module to the attribute-relation file format (ARFF) used by Weka [
In addition, the LFEM uses a different extraction method for each feature. It uses PCA for feature extraction. In PCA, the number of principal components is less than or equal to the number of original variables. The ULRM uses a set of trained data for recognizing location. In this section, we discuss the data format for data training and that of the collected data. In addition, the ULRM shows the location recognition results based on real-time data extracted from the LFEM module.
This section describes the elements of the LDCM. Figure
LDCM.
The wireless sensor node loads data from the data sampler program and sensor board. Thus, the sensor nodes can acquire environmental data from the sensor board. While the data (temperature, humidity, light, and sound data) are being sent, the WSN can also send the data to the sink node through a wireless link by using a sampler program. The wireless link operates in the half-duplex transmission mode. The sink node delivers sensor data to the base station and the sensor network interface through a serial link. The sink node can also acquire environmental data directly from the installed data sampler and sensor board, but not through the wireless sensor node. The sink node has a high-frequency data sampler for sampling high-frequency data effectively. Two types of samplers, a high-frequency sampler and a low-frequency sampler, are used because of the very large amount of processing required for high-frequency data.
The sensor network interface links the sensor network to a base station. The hardware interface, such as USB or RS-232, uses a common serial link. On the other hand, the software interface has a device driver and a system application programming interface (API) for processing data received from the serial link. The location data collector saves environmental data in the data file of the training set.
This training set is created after the data file is given as the input to the LFEM, and it is used by the LFEM for training the ULRM with the feature extraction process. The LDCM interface provides an API, which can be used to obtain environmental data at the user’s location. In the next section, the data extraction method will be explained.
In our system, the LFEM performs data extraction. The structure of the module is shown in Figure
LFEM.
For collecting high-frequency data, the PSD should be used. Sound data and the top five principal component data are then extracted through frequency domain conversion. These real-time data are provided as input to the LFEM interface. They are used for feature extraction in the ULRM during user localization. The LFEM then creates a feature component on the basis of these data.
Figure
ULRM.
In the first recognition test, the feature data can be sent to the location data classifier through the user location recognizer. The recognizer uses k-NN as the location data classifier. The k-NN classification was developed in view of the need for performing discriminant analysis when reliable parametric estimates of probability densities are not available. This classifier is traditionally based on the Euclidean distance between a test sample and specified training samples. k-NN is an algorithm for measuring the distance between bound objects from the value of
Figure
Location feature extraction and recognition procedure.
The LFEM can extract features. For example, assume that we apply PCA to the collected sound and light data. The data are then analyzed using PSD. In spectrum analysis, PSD of data whose analysis element is limitless is used. Fourier transform is used to express limitless data as power per hertz. This representation is often simply called the power spectrum of the data. Intuitively, the spectral density measures the frequency content of a stochastic process and helps identify periodicities. Thus, different extraction methods are applied to different types of data. In addition, PCA is applied to data for high-speed analysis. PCA is a mathematical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. The number of principal components is less than or equal to the number of original variables. This transformation is defined in such a way that the first principal component has the largest possible variance, and each succeeding component has the highest variance possible under the constraint that it is orthogonal to the preceding components. The principal components are guaranteed to be independent only if the dataset is jointly and normally distributed. PCA is sensitive to the relative scaling of the original variables. We perform PCA on and partial characteristics from the sound and light data.
The lower part of Figure
Various software and hardware tools are used in our system. Table
Implementation environments.
Operating system | (i) Location recognition system: Windows Vista |
(ii) Wireless sensor node: TinyOS | |
| |
Programming language | (i) Location recognition system: Java |
(ii) Wireless sensor node: nesC | |
| |
Software tools | (i) Location data feature extraction: MATLAB |
(ii) Location data classification: Weka | |
| |
Hardware | (i) Location recognition system: Intel 2.0 Hz PC |
(ii) Wireless sensor node: Hmote2420 |
The Hmote2420 sensor and TinyOS were used in the LCDM. Hmote2420 was used to collect environmental data and information at the base station. TinyOS was used to deliver the collected data into base station. In addition to the LFEM, we used a computer, a sensor node, a Java platform, and MATLAB to extract features from the collected data. The ULRM used the Java platform to show the recognized user’s position, which was determined from the collected features. In addition, the k-NN algorithm was used for location recognition.
Table
Environmental data collection methods.
Rec/sec | Sampling rate (Hz) | Duration |
samples/rec | Type of | |
---|---|---|---|---|---|
Temperature | 5/10 | 1 | 4 | 4 | Std. |
Humidity | 5/10 | 1 | 4 | 4 | Std. |
Light | 5/10 | 2048 | 0.5 | 1024 | High Freq. |
Sound | 5/10 | 8000 | 4 | 32000 | High Freq. |
The format of environmental datasets used in this study was ARFF. Temperature, humidity, light, and sound data were used to build training datasets, as explained in Section
Feature extraction from a dataset involves different processes, depending on the sampling rate of the dataset (see Figure
On the other hand, environmental data sampled at a low frequency, such as temperature and humidity data, can be directly used as representative features for each location. Therefore, PCA need not be performed on these datasets. Figure
ARFF format of dataset file.
In our experiments, data were collected from different places in Konkuk University (Figure
Environments considered in the experiments.
First, we collected 100 datasets from each place by using the sensor. A total of 600 datasets were collected from the six locations. Second, the collected data were classified into high- and low-frequency data. The classified data were extracted using the feature extraction method of MATLAB. The extracted data were then converted into formats compatible with Weka. Next, ten more datasets were collected at the same time and at the same locations. Finally, our system used the collected data to recognize user locations.
After training the localization classifier, we collected 10 additional feature datasets from different places at each location to test the classifier. The sensor’s location was then identified using the 10 datasets.
The average localization accuracy
Table
Offline localization experimental results.
Test data | Classified | |||||
---|---|---|---|---|---|---|
Lobby | Laboratory | Toilet | Cafeteria | Bank | Bookstore | |
Lobby |
|
0 | 1 | 0 | 8 | 0 |
Laboratory | 0 |
|
0 | 0 | 0 | 1 |
Toilet | 1 | 0 |
|
0 | 5 | 0 |
Cafeteria | 0 | 0 | 0 |
|
1 | 0 |
Bank | 4 | 0 | 2 | 2 |
|
0 |
Bookstore | 0 | 3 | 0 | 0 | 0 |
|
In the table, the correct location data are shown in bold font. High localization accuracy is achieved for the laboratory and cafeteria data because of the correct classification of features. This implies that a high localization accuracy will be obtained in places where the features are well separated. Errors in recognition occasionally occur in the case of the lobby and bank. This implies that these two environments are similar in temperature, humidity, light, and sound.
Table
Real-time localization experimental results.
Location | localization accuracy |
---|---|
Laboratory | 76.7% |
Lobby | 83.3% |
Toilet | 100% |
Cafeteria | 86.7% |
Bank | 93.3% |
Bookstore | 53.3% |
| |
Average | 82.2% |
The classifier confused the bookstore with the lobby. This occurred because both the locations have similar light and temperature conditions. However, in the case of the toilet, because of the high humidity, the recognition results showed high localization accuracy. Finally, we can improve the localization performance of our system further by using additional types of environmental data, especially for environments with similar conditions with regard to temperature, humidity, light, and sound.
In this paper, we have proposed a novel location recognition method for wireless sensor nodes. The method involves the classification of environmental data features using the k-NN localization data classifier. We performed localization experiments in an actual test environment by using the proposed method. The experimental results indicated high localization accuracy. In a real-time recognition experiment, the localization accuracy was found to be 82.2%. This value indicates that environmental data can be used for the purpose of location recognition. It also shows the importance of environmental data recognition in location recognition. Our future research will focus on combining the proposed location recognition method and other localization methods, such as RSS pattern recognition methods. Furthermore, we intend using a modified version of PCA [
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), which is funded by the Ministry of Education, Science and Technology (Grant no. 2012006817).